main

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

.gitignore

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+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.pyc
+*.pyo
+*.pyd
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak
+venv.bak
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# VS Code
+.vscode/
+
+# FYP files
+<<<<<<< HEAD
+=======
+fyp report .docx
+fyp video.mp4
+>>>>>>> ff272df21f75042ffc26863b5f3a0d584f9977cb
+

FYP/GameEnv.py

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+import pygame
+import math
+from Walls import Wall
+from Walls import getWalls
+from Goals import Goal
+from Goals import getGoals
+
+
+
+GOALREWARD = 1
+LIFE_REWARD = 0
+PENALTY = -1
+
+
+def distance(pt1, pt2):
+    return(((pt1.x - pt2.x)**2 + (pt1.y - pt2.y)**2)**0.5)
+
+def rotate(origin,point,angle):
+    qx = origin.x + math.cos(angle) * (point.x - origin.x) - math.sin(angle) * (point.y - origin.y)
+    qy = origin.y + math.sin(angle) * (point.x - origin.x) + math.cos(angle) * (point.y - origin.y)
+    q = myPoint(qx, qy)
+    return q
+
+def rotateRect(pt1, pt2, pt3, pt4, angle):
+
+    pt_center = myPoint((pt1.x + pt3.x)/2, (pt1.y + pt3.y)/2)
+
+    pt1 = rotate(pt_center,pt1,angle)
+    pt2 = rotate(pt_center,pt2,angle)
+    pt3 = rotate(pt_center,pt3,angle)
+    pt4 = rotate(pt_center,pt4,angle)
+
+    return pt1, pt2, pt3, pt4
+
+class myPoint:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        
+class myLine:
+    def __init__(self, pt1, pt2):
+        self.pt1 = myPoint(pt1.x, pt1.y)
+        self.pt2 = myPoint(pt2.x, pt2.y)
+
+class Ray:
+    def __init__(self,x,y,angle):
+        self.x = x
+        self.y = y
+        self.angle = angle
+
+    def cast(self, wall):
+        x1 = wall.x1 
+        y1 = wall.y1
+        x2 = wall.x2
+        y2 = wall.y2
+
+        vec = rotate(myPoint(0,0), myPoint(0,-1000), self.angle)
+        
+        x3 = self.x
+        y3 = self.y
+        x4 = self.x + vec.x
+        y4 = self.y + vec.y
+
+        den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+            
+        if(den == 0):
+            den = 0
+        else:
+            t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / den
+            u = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den
+
+            if t > 0 and t < 1 and u < 1 and u > 0:
+                pt = myPoint(math.floor(x1 + t * (x2 - x1)), math.floor(y1 + t * (y2 - y1)))
+                return(pt)
+
+ 
+
+class Car:
+    def __init__(self, x, y):
+        self.pt = myPoint(x, y)
+        self.x = x
+        self.y = y
+        self.width = 14
+        self.height = 30
+
+        self.points = 0
+
+        self.original_image = pygame.image.load("car.png").convert()
+        self.image = self.original_image  # This will reference the rotated image.
+        self.image.set_colorkey((0,0,0))
+        self.rect = self.image.get_rect().move(self.x, self.y)
+
+        self.angle = math.radians(180)
+        self.soll_angle = self.angle
+
+        self.dvel = 1
+        self.vel = 0
+        self.velX = 0
+        self.velY = 0
+        self.maxvel = 15 # before 15
+
+        self.angle = math.radians(180)
+        self.soll_angle = self.angle
+
+        self.pt1 = myPoint(self.pt.x - self.width / 2, self.pt.y - self.height / 2)
+        self.pt2 = myPoint(self.pt.x + self.width / 2, self.pt.y - self.height / 2)
+        self.pt3 = myPoint(self.pt.x + self.width / 2, self.pt.y + self.height / 2)
+        self.pt4 = myPoint(self.pt.x - self.width / 2, self.pt.y + self.height / 2)
+
+        self.p1 = self.pt1
+        self.p2 = self.pt2
+        self.p3 = self.pt3
+        self.p4 = self.pt4
+
+        self.distances = []
+    
+
+    def action(self, choice):
+        if choice == 0:
+            pass
+        elif choice == 1:
+            self.accelerate(self.dvel)
+        elif choice == 8:
+            self.accelerate(self.dvel)
+            self.turn(1)
+        elif choice == 7:
+            self.accelerate(self.dvel)
+            self.turn(-1)
+        elif choice == 4:
+            self.accelerate(-self.dvel)
+        elif choice == 5:
+            self.accelerate(-self.dvel)
+            self.turn(1)
+        elif choice == 6:
+            self.accelerate(-self.dvel)
+            self.turn(-1)
+        elif choice == 3:
+            self.turn(1)
+        elif choice == 2:
+            self.turn(-1)
+        pass
+    
+    def accelerate(self,dvel):
+        dvel = dvel * 2
+
+        self.vel = self.vel + dvel
+
+        if self.vel > self.maxvel:
+            self.vel = self.maxvel
+        
+        if self.vel < -self.maxvel:
+            self.vel = -self.maxvel
+        
+        
+    def turn(self, dir):
+        self.soll_angle = self.soll_angle + dir * math.radians(15)
+    
+    def update(self):
+
+        #drifting code         
+        self.angle = self.soll_angle
+
+        vec_temp = rotate(myPoint(0,0), myPoint(0,self.vel), self.angle)
+        self.velX, self.velY = vec_temp.x, vec_temp.y
+
+        self.x = self.x + self.velX
+        self.y = self.y + self.velY
+
+        self.rect.center = self.x, self.y
+
+        self.pt1 = myPoint(self.pt1.x + self.velX, self.pt1.y + self.velY)
+        self.pt2 = myPoint(self.pt2.x + self.velX, self.pt2.y + self.velY)
+        self.pt3 = myPoint(self.pt3.x + self.velX, self.pt3.y + self.velY)
+        self.pt4 = myPoint(self.pt4.x + self.velX, self.pt4.y + self.velY)
+
+        self.p1 ,self.p2 ,self.p3 ,self.p4  = rotateRect(self.pt1, self.pt2, self.pt3, self.pt4, self.soll_angle)
+
+        self.image = pygame.transform.rotate(self.original_image, 90 - self.soll_angle * 180 / math.pi)
+        x, y = self.rect.center  # Save its current center.
+        self.rect = self.image.get_rect()  # Replace old rect with new rect.
+        self.rect.center = (x, y)
+
+    def cast(self, walls):
+
+        ray1 = Ray(self.x, self.y, self.soll_angle)
+        ray2 = Ray(self.x, self.y, self.soll_angle - math.radians(30))
+        ray3 = Ray(self.x, self.y, self.soll_angle + math.radians(30))
+        ray4 = Ray(self.x, self.y, self.soll_angle + math.radians(45))
+        ray5 = Ray(self.x, self.y, self.soll_angle - math.radians(45))
+        ray6 = Ray(self.x, self.y, self.soll_angle + math.radians(90))
+        ray7 = Ray(self.x, self.y, self.soll_angle - math.radians(90))
+        ray8 = Ray(self.x, self.y, self.soll_angle + math.radians(180))
+
+        ray9 = Ray(self.x, self.y, self.soll_angle + math.radians(10))
+        ray10 = Ray(self.x, self.y, self.soll_angle - math.radians(10))
+        ray11 = Ray(self.x, self.y, self.soll_angle + math.radians(135))
+        ray12 = Ray(self.x, self.y, self.soll_angle - math.radians(135))
+        ray13 = Ray(self.x, self.y, self.soll_angle + math.radians(20))
+        ray14 = Ray(self.x, self.y, self.soll_angle - math.radians(20))
+
+        ray15 = Ray(self.p1.x,self.p1.y, self.soll_angle + math.radians(90))
+        ray16 = Ray(self.p2.x,self.p2.y, self.soll_angle - math.radians(90))
+
+        ray17 = Ray(self.p1.x,self.p1.y, self.soll_angle + math.radians(0))
+        ray18 = Ray(self.p2.x,self.p2.y, self.soll_angle - math.radians(0))
+
+        self.rays = []
+        self.rays.append(ray1)
+        self.rays.append(ray2)
+        self.rays.append(ray3)
+        self.rays.append(ray4)
+        self.rays.append(ray5)
+        self.rays.append(ray6)
+        self.rays.append(ray7)
+        self.rays.append(ray8)
+
+        self.rays.append(ray9)
+        self.rays.append(ray10)
+        self.rays.append(ray11)
+        self.rays.append(ray12)
+        self.rays.append(ray13)
+        self.rays.append(ray14)
+
+        self.rays.append(ray15)
+        self.rays.append(ray16)
+
+        self.rays.append(ray17)
+        self.rays.append(ray18)
+
+
+        observations = []
+        self.closestRays = []
+
+        for ray in self.rays:
+            closest = None #myPoint(0,0)
+            record = math.inf
+            for wall in walls:
+                pt = ray.cast(wall)
+                if pt:
+                    dist = distance(myPoint(self.x, self.y),pt)
+                    if dist < record:
+                        record = dist
+                        closest = pt
+
+            if closest: 
+                #append distance for current ray 
+                self.closestRays.append(closest)
+                observations.append(record)
+               
+            else:
+                observations.append(1000)
+
+        for i in range(len(observations)):
+            #invert observation values 0 is far away 1 is close
+            observations[i] = ((1000 - observations[i]) / 1000)
+
+        observations.append(self.vel / self.maxvel)
+        return observations
+
+    def collision(self, wall):
+
+        line1 = myLine(self.p1, self.p2)
+        line2 = myLine(self.p2, self.p3)
+        line3 = myLine(self.p3, self.p4)
+        line4 = myLine(self.p4, self.p1)
+
+        x1 = wall.x1 
+        y1 = wall.y1
+        x2 = wall.x2
+        y2 = wall.y2
+
+        lines = []
+        lines.append(line1)
+        lines.append(line2)
+        lines.append(line3)
+        lines.append(line4)
+
+        for li in lines:
+            
+            x3 = li.pt1.x
+            y3 = li.pt1.y
+            x4 = li.pt2.x
+            y4 = li.pt2.y
+
+            den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+            
+            if(den == 0):
+                den = 0
+            else:
+                t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / den
+                u = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den
+
+                if t > 0 and t < 1 and u < 1 and u > 0:
+                    return(True)
+        
+        return(False)
+    
+    def score(self, goal):
+        
+        line1 = myLine(self.p1, self.p3)
+
+        vec = rotate(myPoint(0,0), myPoint(0,-50), self.angle)
+        line1 = myLine(myPoint(self.x,self.y),myPoint(self.x + vec.x, self.y + vec.y))
+
+        x1 = goal.x1 
+        y1 = goal.y1
+        x2 = goal.x2
+        y2 = goal.y2
+            
+        x3 = line1.pt1.x
+        y3 = line1.pt1.y
+        x4 = line1.pt2.x
+        y4 = line1.pt2.y
+
+        den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+        
+        if(den == 0):
+            den = 0
+        else:
+            t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / den
+            u = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den
+
+            if t > 0 and t < 1 and u < 1 and u > 0:
+                pt = math.floor(x1 + t * (x2 - x1)), math.floor(y1 + t * (y2 - y1))
+
+                d = distance(myPoint(self.x, self.y), myPoint(pt[0], pt[1]))
+                if d < 20:
+                    #pygame.draw.circle(win, (0,255,0), pt, 5)
+                    self.points += GOALREWARD
+                    return(True)
+
+        return(False)
+
+    def reset(self):
+
+        self.x = 50
+        self.y = 300
+        self.velX = 0
+        self.velY = 0
+        self.vel = 0
+        self.angle = math.radians(180)
+        self.soll_angle = self.angle
+        self.points = 0
+
+        self.pt1 = myPoint(self.pt.x - self.width / 2, self.pt.y - self.height / 2)
+        self.pt2 = myPoint(self.pt.x + self.width / 2, self.pt.y - self.height / 2)
+        self.pt3 = myPoint(self.pt.x + self.width / 2, self.pt.y + self.height / 2)
+        self.pt4 = myPoint(self.pt.x - self.width / 2, self.pt.y + self.height / 2)
+
+        self.p1 = self.pt1
+        self.p2 = self.pt2
+        self.p3 = self.pt3
+        self.p4 = self.pt4
+
+    def draw(self, win):
+        win.blit(self.image, self.rect)
+  
+
+class RacingEnv:
+
+    def __init__(self):
+        pygame.init()
+        self.font = pygame.font.Font(pygame.font.get_default_font(), 36)
+
+        self.fps = 120
+        self.width = 1000
+        self.height = 600
+        self.history = []
+
+        self.screen = pygame.display.set_mode((self.width, self.height))
+        pygame.display.set_caption("IMS using DDQN")
+        self.screen.fill((0,0,0))
+        self.back_image = pygame.image.load("track.png").convert()
+        self.back_rect = self.back_image.get_rect().move(0, 0)
+        self.action_space = None
+        self.observation_space = None
+        self.game_reward = 0
+        self.score = 0
+ 
+        self.reset()
+
+
+    def reset(self):
+        self.screen.fill((0, 0, 0))
+
+        self.car = Car(50, 300)
+        self.walls = getWalls()
+        self.goals = getGoals()
+        self.game_reward = 0
+
+    def step(self, action):
+
+        done = False
+        self.car.action(action)
+        self.car.update()
+        reward = LIFE_REWARD
+
+        # Check if car passes Goal and scores
+        index = 1
+        for goal in self.goals:
+            
+            if index > len(self.goals):
+                index = 1
+            if goal.isactiv:
+                if self.car.score(goal):
+                    goal.isactiv = False
+                    self.goals[index-2].isactiv = True
+                    reward += GOALREWARD
+
+            index = index + 1
+
+        #check if car crashed in the wall
+        for wall in self.walls:
+            if self.car.collision(wall):
+                reward += PENALTY
+                done = True
+
+        new_state = self.car.cast(self.walls)
+        #normalize states
+        if done:
+            new_state = None
+
+        return new_state, reward, done
+
+    def render(self, action):
+
+        DRAW_WALLS = True
+        DRAW_GOALS = True
+        DRAW_RAYS = True
+
+        pygame.time.delay(10)
+
+        self.clock = pygame.time.Clock()
+        self.screen.fill((0, 0, 0))
+
+        self.screen.blit(self.back_image, self.back_rect)
+        # Draw checkered start line (black and white boxes)
+        start_x = 50  # same as Car's starting x
+        start_y = 300  # same as Car's starting y
+        line_width = 80
+        line_height = 20
+        num_squares = 16  # Number of squares in the checkered line
+        square_width = line_width // num_squares
+        for i in range(num_squares):
+            color = (255, 255, 255) if i % 2 == 0 else (0, 0, 0)
+            rect_x = start_x - line_width // 2 + i * square_width
+            pygame.draw.rect(self.screen, color, (rect_x, start_y - line_height // 2, square_width, line_height))
+        if DRAW_WALLS:
+            for wall in self.walls:
+                wall.draw(self.screen)
+        
+        if DRAW_GOALS:
+            for goal in self.goals:
+                goal.draw(self.screen)
+                if goal.isactiv:
+                    goal.draw(self.screen)
+        
+        self.car.draw(self.screen)
+
+        if DRAW_RAYS:
+            i = 0
+            for pt in self.car.closestRays:
+                pygame.draw.circle(self.screen, (0,0,255), (pt.x, pt.y), 5)
+                i += 1
+                if i < 15:
+                    pygame.draw.line(self.screen, (255,255,255), (self.car.x, self.car.y), (pt.x, pt.y), 1)
+                elif i >=15 and i < 17:
+                    pygame.draw.line(self.screen, (255,255,255), ((self.car.p1.x + self.car.p2.x)/2, (self.car.p1.y + self.car.p2.y)/2), (pt.x, pt.y), 1)
+                elif i == 17:
+                    pygame.draw.line(self.screen, (255,255,255), (self.car.p1.x , self.car.p1.y ), (pt.x, pt.y), 1)
+                else:
+                    pygame.draw.line(self.screen, (255,255,255), (self.car.p2.x, self.car.p2.y), (pt.x, pt.y), 1)
+
+        #render controll
+        pygame.draw.rect(self.screen,(255,255,255),(800, 100, 40, 40),2)
+        pygame.draw.rect(self.screen,(255,255,255),(850, 100, 40, 40),2)
+        pygame.draw.rect(self.screen,(255,255,255),(900, 100, 40, 40),2)
+        pygame.draw.rect(self.screen,(255,255,255),(850, 50, 40, 40),2)
+
+        if action == 4:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 50, 40, 40)) 
+        elif action == 6:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 50, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(800, 100, 40, 40))
+        elif action == 5:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 50, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(900, 100, 40, 40))
+        elif action == 1:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 100, 40, 40)) 
+        elif action == 8:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 100, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(800, 100, 40, 40))
+        elif action == 7:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 100, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(900, 100, 40, 40))
+        elif action == 2:
+            pygame.draw.rect(self.screen,(0,255,0),(800, 100, 40, 40))
+        elif action == 3:
+            pygame.draw.rect(self.screen,(0,255,0),(900, 100, 40, 40))
+
+        # score
+        text_surface = self.font.render(f'Points {self.car.points}', True, pygame.Color('green'))
+        self.screen.blit(text_surface, dest=(0, 0))
+        # speed
+        text_surface = self.font.render(f'Speed {self.car.vel*-1}', True, pygame.Color('green'))
+        self.screen.blit(text_surface, dest=(800, 0))
+
+        self.clock.tick(self.fps)
+        pygame.display.update()
+
+    def close(self):
+        pygame.quit()
+
+
+

FYP/Goals.py

@@ -0,0 +1,99 @@
+import pygame
+
+class Goal:
+    def __init__(self, x1, y1, x2, y2):
+        self.x1 = x1
+        self.y1 = y1
+        self.x2 = x2
+        self.y2 = y2
+
+        self.isactiv = False
+    
+    def draw(self, win):
+        pygame.draw.line(win, (0,255,0), (self.x1, self.y1), (self.x2, self.y2), 2)
+        if self.isactiv:
+            pygame.draw.line(win, (255,0,0), (self.x1, self.y1), (self.x2, self.y2), 2)
+
+# the file of shame
+def getGoals():
+    goals = []
+
+    goal1 = Goal(0,200,120,200)
+    goal2 = Goal(0,100,120,150)
+    goal2_5 = Goal(0,0,150,130)
+    goal3 = Goal(120,0,170,120)
+    goal3_5 = Goal(200,0,200,120)
+    goal4 = Goal(270,0,270,110)
+    goal4_5 = Goal(350,0,350,110)
+    goal5 = Goal(450,0,450,110)
+    goal5_5 = Goal(525,0,525,110)
+    goal6 = Goal(600,0,550,130)
+    goal6_5 = Goal(550,130,700,60)
+    goal7 = Goal(550,130,700,130)
+    goal7_5 = Goal(550,130,650,200)
+    goal8 = Goal(550,130,570,240)
+    goal9 = Goal(410,130,430,260)
+    goal9_5 = Goal(430,260,300,350)
+    goal10 = Goal(430,260,260,260)
+    goal10_5 = Goal(430,260,280,180)
+    goal11 = Goal(430,260,400,400)
+    goal12 = Goal(550,260,570,400)
+    goal13 = Goal(750,400,650,200)
+    goal14 = Goal(750,400,800,160)
+    goal15 = Goal(750,400,950,240)
+    goal16 = Goal(750,400,980,440)
+    goal17 = Goal(750,400,900,600)
+    goal18 = Goal(750,460,750,600)
+    goal19 = Goal(670,460,670,600)
+    goal19_5 = Goal(590,460,590,600)
+    goal20 = Goal(510,460,510,600)
+    goal20_5 = Goal(430,460,430,600)
+    goal21 = Goal(350,460,350,600)
+    goal21_5 = Goal(280,460,278,600)
+    goal22 = Goal(210,460,190,600)
+    goal22_5 = Goal(80,600,175,440)
+    goal23 = Goal(150,420,0,570)
+    goal23_5 = Goal(0,450,130,400)
+    goal24 = Goal(0,380,130,380)
+
+    goals.append(goal1)
+    goals.append(goal2)
+    goals.append(goal2_5)
+    goals.append(goal3)
+    goals.append(goal3_5)
+    goals.append(goal4)
+    goals.append(goal4_5)
+    goals.append(goal5)
+    goals.append(goal5_5)
+    goals.append(goal6)
+    goals.append(goal6_5)
+    goals.append(goal7)
+    goals.append(goal7_5)
+    goals.append(goal8)
+    goals.append(goal9)
+    goals.append(goal10_5)
+    goals.append(goal10)
+    goals.append(goal9_5)
+    goals.append(goal11)
+    goals.append(goal12)
+    goals.append(goal13)
+    goals.append(goal14)
+    goals.append(goal15)
+    goals.append(goal16)
+    goals.append(goal17)
+    goals.append(goal18)
+    goals.append(goal19)
+    goals.append(goal19_5)
+    goals.append(goal20)
+    goals.append(goal20_5)
+    goals.append(goal21)
+    goals.append(goal21_5)
+    goals.append(goal22)
+    goals.append(goal22_5)
+    goals.append(goal23)
+    goals.append(goal23_5)
+    goals.append(goal24)
+
+    goals[len(goals)-1].isactiv = True
+
+    return(goals)

FYP/Walls.py

@@ -0,0 +1,122 @@
+import pygame
+
+class Wall:
+    def __init__(self, x1, y1, x2, y2):
+        self.x1 = x1
+        self.y1 = y1
+        self.x2 = x2
+        self.y2 = y2
+    
+    def draw(self, win):
+        pygame.draw.line(win, (255,255,255), (self.x1, self.y1), (self.x2, self.y2), 5)
+
+def getWalls():
+    walls = []
+
+    wall1 = Wall(12, 451, 15, 130)
+    wall2 = Wall(15, 130, 61, 58)
+    wall3 = Wall(61, 58, 149, 14)
+    wall4 = Wall(149, 14, 382, 20)
+    wall5 = Wall(382, 20, 549, 31)
+    wall6 = Wall(549, 31, 636, 58)
+    wall7 = Wall(636, 58, 678, 102)
+    wall8 = Wall(678, 102, 669, 167)
+    wall9 = Wall(669, 167, 600, 206)
+    wall10 = Wall(600, 206, 507, 214)
+    wall11 = Wall(507, 214, 422, 232)
+    wall12 = Wall(422, 232, 375, 263)
+    wall13 = Wall(375, 263, 379, 283)
+    wall14 = Wall(379, 283, 454, 299)
+    wall15 = Wall(454, 299, 613, 286)
+    wall16 = Wall(613, 286, 684, 238)
+    wall17 = Wall(684, 238, 752, 180)
+    wall18 = Wall(752, 180, 862, 185)
+    wall19 = Wall(862, 185, 958, 279)
+    wall20 = Wall(958, 279, 953, 410)
+    wall21 = Wall(953, 410, 925, 505)
+    wall22 = Wall(925, 505, 804, 566)
+    wall23 = Wall(804, 566, 150, 570)
+    wall24 = Wall(150, 570, 46, 529)
+    wall25 = Wall(46, 529, 12, 451)
+    wall27 = Wall(104, 436, 96, 161)
+    wall28 = Wall(96, 161, 122, 122)
+    wall29 = Wall(122, 122, 199, 91)
+    wall30 = Wall(199, 91, 376, 94)
+    wall31 = Wall(376, 94, 469, 100)
+    wall32 = Wall(469, 100, 539, 102)
+    wall33 = Wall(539, 102, 585, 121)
+    wall34 = Wall(585, 121, 585, 139)
+    wall35 = Wall(585, 139, 454, 158)
+    wall36 = Wall(454, 158, 352, 183)
+    wall37 = Wall(352, 183, 293, 239)
+    wall38 = Wall(293, 239, 294, 318)
+    wall39 = Wall(294, 318, 361, 357)
+    wall40 = Wall(361, 357, 490, 373)
+    wall41 = Wall(490, 373, 671, 359)
+    wall42 = Wall(671, 359, 752, 300) #
+    wall43 = Wall(752, 300, 812, 310)#
+    wall44 = Wall(812, 310, 854, 369)
+    wall45 = Wall(854, 369, 854, 429)
+    wall46 = Wall(854, 429, 754, 483)
+    wall47 = Wall(754, 483, 192, 489)
+    wall48 = Wall(192, 489, 104, 436)
+
+    walls.append(wall1)
+    walls.append(wall2)
+    walls.append(wall3)
+    walls.append(wall4)
+    walls.append(wall5)
+    walls.append(wall6)
+    walls.append(wall7)
+    walls.append(wall8)
+    walls.append(wall9)
+    walls.append(wall10)
+    walls.append(wall11)
+    walls.append(wall12)
+    walls.append(wall13)
+    walls.append(wall14)
+    walls.append(wall15)
+    walls.append(wall16)
+    walls.append(wall17)
+    walls.append(wall18)
+    walls.append(wall19)
+    walls.append(wall20)
+    walls.append(wall21)
+    walls.append(wall22)
+    walls.append(wall23)
+    walls.append(wall24)
+    walls.append(wall25)
+
+    walls.append(wall27)
+    walls.append(wall28)
+    walls.append(wall29)
+    walls.append(wall30)
+    walls.append(wall31)
+    walls.append(wall32)
+    walls.append(wall33)
+    walls.append(wall34)
+    walls.append(wall35)
+    walls.append(wall36)
+    walls.append(wall37)
+    walls.append(wall38)
+    walls.append(wall39)
+    walls.append(wall40)
+    walls.append(wall41)
+    walls.append(wall42)
+    walls.append(wall43)
+    walls.append(wall44)
+    walls.append(wall45)
+    walls.append(wall46)
+    walls.append(wall47)
+    walls.append(wall48)
+
+    return(walls)
+
+
+
+
+
+
+
+
+

FYP/ddqn_keras.py

@@ -0,0 +1,126 @@
+# File: ddqn_keras.py
+
+import numpy as np
+import tensorflow as tf
+from keras.models import Sequential, load_model
+from keras.layers import Dense
+from keras.optimizers import Adam
+
+class ReplayBuffer:
+    def __init__(self, max_size, input_shape, n_actions, discrete=False):
+        self.mem_size = max_size
+        self.mem_cntr = 0
+        self.discrete = discrete
+        self.state_memory = np.zeros((self.mem_size, input_shape))
+        self.new_state_memory = np.zeros((self.mem_size, input_shape))
+        dtype = np.int8 if self.discrete else np.float32
+        self.action_memory = np.zeros((self.mem_size, n_actions), dtype=dtype)
+        self.reward_memory = np.zeros(self.mem_size)
+        self.terminal_memory = np.zeros(self.mem_size, dtype=np.float32)
+
+    def store_transition(self, state, action, reward, state_, done):
+        index = self.mem_cntr % self.mem_size
+        self.state_memory[index] = state
+        self.new_state_memory[index] = state_
+        if self.discrete:
+            actions = np.zeros(self.action_memory.shape[1])
+            actions[action] = 1.0
+            self.action_memory[index] = actions
+        else:
+            self.action_memory[index] = action
+        self.reward_memory[index] = reward
+        self.terminal_memory[index] = 1 - done
+        self.mem_cntr += 1
+
+    def sample_buffer(self, batch_size):
+        max_mem = min(self.mem_cntr, self.mem_size)
+        batch = np.random.choice(max_mem, batch_size)
+
+        states = self.state_memory[batch]
+        actions = self.action_memory[batch]
+        rewards = self.reward_memory[batch]
+        states_ = self.new_state_memory[batch]
+        terminal = self.terminal_memory[batch]
+
+        return states, actions, rewards, states_, terminal
+
+class DDQNAgent:
+    def __init__(self, alpha, gamma, n_actions, epsilon, batch_size,
+                 input_dims, epsilon_dec=0.999995, epsilon_end=0.10,
+                 mem_size=25000, fname='ddqn_model.keras', replace_target=25):
+        self.action_space = [i for i in range(n_actions)]
+        self.n_actions = n_actions
+        self.gamma = gamma
+        self.epsilon = epsilon
+        self.epsilon_dec = epsilon_dec
+        self.epsilon_min = epsilon_end
+        self.batch_size = batch_size
+        self.model_file = fname
+        self.replace_target = replace_target
+        self.memory = ReplayBuffer(mem_size, input_dims, n_actions, discrete=True)
+       
+        self.brain_eval = Brain(input_dims, n_actions, batch_size)
+        self.brain_target = Brain(input_dims, n_actions, batch_size)
+
+    def remember(self, state, action, reward, new_state, done):
+        self.memory.store_transition(state, action, reward, new_state, done)
+
+    def choose_action(self, state):
+        state = np.array(state)[np.newaxis, :]
+        rand = np.random.random()
+        if rand < self.epsilon:
+            action = np.random.choice(self.action_space)
+        else:
+            actions = self.brain_eval.model.predict(state, verbose=0)
+            action = np.argmax(actions)
+        return action
+
+    def learn(self):
+        if self.memory.mem_cntr > self.batch_size:
+            state, action, reward, new_state, done = self.memory.sample_buffer(self.batch_size)
+            action_indices = np.dot(action, np.array(self.action_space, dtype=np.int8))
+            
+            q_next = self.brain_target.model.predict(new_state, verbose=0)
+            q_eval = self.brain_eval.model.predict(new_state, verbose=0)
+            q_pred = self.brain_eval.model.predict(state, verbose=0)
+
+            max_actions = np.argmax(q_eval, axis=1)
+            
+            q_target = np.copy(q_pred)
+            batch_index = np.arange(self.batch_size, dtype=np.int32)
+            q_target[batch_index, action_indices] = reward + self.gamma * q_next[batch_index, max_actions.astype(int)] * done
+
+            self.brain_eval.model.fit(state, q_target, verbose=0)
+            self.epsilon = max(self.epsilon * self.epsilon_dec, self.epsilon_min)
+
+    def update_network_parameters(self):
+        self.brain_target.copy_weights(self.brain_eval)
+
+    def save_model(self):
+        self.brain_eval.model.save(self.model_file)  # Save using .keras format
+        
+    def load_model(self):
+        self.brain_eval.model = tf.keras.models.load_model(self.model_file)
+        self.brain_target.model = tf.keras.models.load_model(self.model_file)
+        if self.epsilon == 0.0:
+            self.update_network_parameters()
+
+class Brain:
+    def __init__(self, NbrStates, NbrActions, batch_size=256):
+        self.NbrStates = NbrStates
+        self.NbrActions = NbrActions
+        self.batch_size = batch_size
+        self.model = self.createModel()
+    
+    def createModel(self):
+        model = Sequential()
+        model.add(Dense(256, activation='relu', input_shape=(self.NbrStates,)))
+        model.add(Dense(self.NbrActions, activation=tf.keras.activations.softmax))
+        model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))
+        return model
+    
+    def copy_weights(self, TrainNet):
+        variables1 = self.model.trainable_variables
+        variables2 = TrainNet.model.trainable_variables
+        for v1, v2 in zip(variables1, variables2):
+            v1.assign(v2.numpy())

FYP/main.py

@@ -0,0 +1,81 @@
+import GameEnv
+import numpy as np
+from ddqn_keras import DDQNAgent
+import pygame
+import os
+import tensorflow as tf
+
+os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
+
+model_path = "ddqn_model.keras"
+
+
+
+TOTAL_GAMETIME = 1000
+N_EPISODES = 10000
+REPLACE_TARGET = 50
+
+game = GameEnv.RacingEnv()
+game.fps = 60
+
+ddqn_agent = DDQNAgent(alpha=0.0005, gamma=0.99, n_actions=5, epsilon=1.00, 
+                        epsilon_end=0.10, epsilon_dec=0.9995, replace_target=REPLACE_TARGET, 
+                        batch_size=512, input_dims=19, fname=model_path)
+
+ddqn_scores = []
+eps_history = []
+best_score = float('-inf')  # Store highest score only
+
+def run():
+    global best_score
+    for e in range(N_EPISODES):
+        game.reset()
+        done = False
+        score = 0
+        counter = 0
+        observation_, reward, done = game.step(0)
+        observation = np.array(observation_)
+        gtime = 0
+        renderFlag = e % 10 == 0 and e > 0  # Render every 10 episodes
+
+        while not done:
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    return
+
+            action = ddqn_agent.choose_action(observation)
+            observation_, reward, done = game.step(action)
+            observation_ = np.array(observation_)
+
+            if reward == 0:
+                counter += 1
+                if counter > 100:
+                    done = True
+            else:
+                counter = 0
+
+            score += reward
+            ddqn_agent.remember(observation, action, reward, observation_, int(done))
+            observation = observation_
+            ddqn_agent.learn()
+            gtime += 1
+            if gtime >= TOTAL_GAMETIME:
+                done = True
+            if renderFlag:
+                game.render(action)
+
+        eps_history.append(ddqn_agent.epsilon)
+        ddqn_scores.append(score)
+        avg_score = np.mean(ddqn_scores[max(0, e-100):(e+1)])
+
+        # Save model only if it achieves a new highest score
+        if score > best_score:
+            best_score = score
+            ddqn_agent.save_model()
+            print(f"🎯 New best score {best_score}! Model saved.")
+
+        if e % REPLACE_TARGET == 0 and e > REPLACE_TARGET:
+            ddqn_agent.update_network_parameters()
+        
+        print(f'Episode: {e}, Reward (Total): {int(score)}, Avg Reward: {int(avg_score)}, Best Reward: {int(best_score)}, Epsilon: {ddqn_agent.epsilon}')
+run()

FYP/main_test_model.py

@@ -0,0 +1,69 @@
+import GameEnv
+import numpy as np
+from ddqn_keras import DDQNAgent
+import pygame
+import tensorflow as tf
+
+# model_path = "ddqn_model.keras"
+model_path="ddqn_model.keras"
+
+try:
+    model = tf.keras.models.load_model(model_path)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f" Failed to load model: {e}")
+
+TOTAL_GAMETIME = 10000
+N_EPISODES = 10000
+REPLACE_TARGET = 10
+
+game = GameEnv.RacingEnv()
+game.fps = 60
+
+ddqn_agent = DDQNAgent(alpha=0.0005, gamma=0.99, n_actions=5, epsilon=0.02, 
+                        epsilon_end=0.01, epsilon_dec=0.999, replace_target=REPLACE_TARGET, 
+                        batch_size=64, input_dims=19, fname=model_path)
+
+ddqn_agent.load_model()
+ddqn_agent.update_network_parameters()
+
+def run():
+    for e in range(N_EPISODES):
+        game.reset()
+        done = False
+        score = 0
+        counter = 0
+        gtime = 0
+        observation_, reward, done = game.step(0)
+        observation = np.array(observation_)
+
+        while not done:
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    return
+
+            action = ddqn_agent.choose_action(observation)
+            observation_, reward, done = game.step(action)
+            observation_ = np.array(observation_)
+
+            if reward == 0:
+                counter += 1
+                if counter > 100:
+                    done = True
+            else:
+                counter = 0
+
+            score += reward
+            observation = observation_
+            gtime += 1
+            if gtime >= TOTAL_GAMETIME:
+                done = True
+            game.render(action)
+            #  if score > best_score:
+            # best_score = score
+            # ddqn_agent.save_model()
+            # print(f" New best score {best_score}! Model saved.")
+
+        print(f"Episode: {e}, Reward: {score}")
+
+run()

FYP/requirements.txt

@@ -0,0 +1,3 @@
+pygame
+numpy
+tensorflow

README.md

@@ -0,0 +1,10 @@
+---
+title: IMS
+emoji: 🔥
+colorFrom: pink
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

fyp1/best_neat_genome.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:166b4658e68cab4f72d9ea7c248081aa13ac34d447a2df4bfe26fbed975c5274
+size 1199

fyp1/config.txt

@@ -0,0 +1,79 @@
+[NEAT]
+fitness_criterion     = max
+fitness_threshold     = 100000000
+pop_size              = 30
+reset_on_extinction   = True
+
+[DefaultGenome]
+# node activation options
+activation_default      = tanh
+activation_mutate_rate  = 0.01
+activation_options      = tanh
+
+# node aggregation options
+aggregation_default     = sum
+aggregation_mutate_rate = 0.01
+aggregation_options     = sum
+
+# node bias options
+bias_init_mean          = 0.0
+bias_init_stdev         = 1.0
+bias_max_value          = 30.0
+bias_min_value          = -30.0
+bias_mutate_power       = 0.5
+bias_mutate_rate        = 0.7
+bias_replace_rate       = 0.1
+
+# genome compatibility options
+compatibility_disjoint_coefficient = 1.0
+compatibility_weight_coefficient   = 0.5
+
+# connection add/remove rates
+conn_add_prob           = 0.5
+conn_delete_prob        = 0.5
+
+# connection enable options
+enabled_default         = True
+enabled_mutate_rate     = 0.01
+
+feed_forward            = True
+initial_connection      = full
+
+# node add/remove rates
+node_add_prob           = 0.2
+node_delete_prob        = 0.2
+
+# network parameters
+num_hidden              = 0
+num_inputs              = 5
+num_outputs             = 4
+
+# node response options
+response_init_mean      = 1.0
+response_init_stdev     = 0.0
+response_max_value      = 30.0
+response_min_value      = -30.0
+response_mutate_power   = 0.0
+response_mutate_rate    = 0.0
+response_replace_rate   = 0.0
+
+# connection weight options
+weight_init_mean        = 0.0
+weight_init_stdev       = 1.0
+weight_max_value        = 30
+weight_min_value        = -30
+weight_mutate_power     = 0.5
+weight_mutate_rate      = 0.8
+weight_replace_rate     = 0.1
+
+[DefaultSpeciesSet]
+compatibility_threshold = 2.0
+
+[DefaultStagnation]
+species_fitness_func = max
+max_stagnation       = 20
+species_elitism      = 2
+
+[DefaultReproduction]
+elitism            = 3
+survival_threshold = 0.2

fyp1/model_testing_NEAT.py

@@ -0,0 +1,35 @@
+import neat
+import pickle
+import os
+
+# Import your simulation function
+from newcar import run_simulation  # Make sure run_simulation accepts ([(genome_id, genome)], config)
+
+# Paths
+config_path = "config.txt"
+genome_path = "best_neat_genome.pkl"
+
+# Load NEAT config
+config = neat.config.Config(
+    neat.DefaultGenome,
+    neat.DefaultReproduction,
+    neat.DefaultSpeciesSet,
+    neat.DefaultStagnation,
+    config_path
+)
+
+# Load the best genome
+if not os.path.exists(genome_path):
+    raise FileNotFoundError(f"Best genome file not found at {genome_path}")
+
+with open(genome_path, "rb") as f:
+    best_genome = pickle.load(f)
+
+# print("\n✅ Best genome loaded successfully!")
+# print(f"Fitness: {best_genome.fitness}")
+
+# Wrap the genome in the expected format: list of tuples (genome_id, genome)
+test_genomes = [(0, best_genome)]
+
+# Run the simulation for testing
+run_simulation(test_genomes, config)

fyp1/newcar.py

@@ -0,0 +1,288 @@
+import math
+import random
+import sys
+import os
+
+import neat
+import pygame
+
+# Constants
+#WIDTH = 1500
+#HEIGHT = 800
+
+WIDTH = 1920
+HEIGHT = 1080
+
+
+
+CAR_SIZE_X = 60    
+CAR_SIZE_Y = 60
+
+BORDER_COLOR = (255, 255, 255, 255) # Color To Crash on Hit
+
+current_generation = 0 # Generation counter
+
+class Car:
+
+    def __init__(self):
+        # Load Car Sprite and Rotate
+        self.sprite = pygame.image.load('car.png').convert() # Convert Speeds Up A Lot
+        self.sprite = pygame.transform.scale(self.sprite, (CAR_SIZE_X, CAR_SIZE_Y))
+        self.rotated_sprite = self.sprite 
+
+        # self.position = [690, 740] # Starting Position
+        self.position = [830, 920] # Starting Position
+        self.angle = 0
+        self.speed = 0
+
+        self.speed_set = False # Flag For Default Speed Later on
+
+        self.center = [self.position[0] + CAR_SIZE_X / 2, self.position[1] + CAR_SIZE_Y / 2] # Calculate Center
+
+        self.radars = [] # List For Sensors / Radars
+        self.drawing_radars = [] # Radars To Be Drawn
+
+        self.alive = True # Boolean To Check If Car is Crashed
+
+        self.distance = 0 # Distance Driven
+        self.time = 0 # Time Passed
+
+    def draw(self, screen):
+        screen.blit(self.rotated_sprite, self.position) # Draw Sprite
+        self.draw_radar(screen) #OPTIONAL FOR SENSORS
+
+    def draw_radar(self, screen):
+        # Optionally Draw All Sensors / Radars
+        for radar in self.radars:
+            position = radar[0]
+            pygame.draw.line(screen, (0, 255, 0), self.center, position, 1)
+            pygame.draw.circle(screen, (0, 255, 0), position, 5)
+
+    def check_collision(self, game_map):
+        self.alive = True
+        for point in self.corners:
+            # If Any Corner Touches Border Color -> Crash
+            # Assumes Rectangle
+            if game_map.get_at((int(point[0]), int(point[1]))) == BORDER_COLOR:
+                self.alive = False
+                break
+
+    def check_radar(self, degree, game_map):
+        length = 0
+        x = int(self.center[0] + math.cos(math.radians(360 - (self.angle + degree))) * length)
+        y = int(self.center[1] + math.sin(math.radians(360 - (self.angle + degree))) * length)
+
+        # While We Don't Hit BORDER_COLOR AND length < 300 (just a max) -> go further and further
+        while not game_map.get_at((x, y)) == BORDER_COLOR and length < 300:
+            length = length + 1
+            x = int(self.center[0] + math.cos(math.radians(360 - (self.angle + degree))) * length)
+            y = int(self.center[1] + math.sin(math.radians(360 - (self.angle + degree))) * length)
+
+        # Calculate Distance To Border And Append To Radars List
+        dist = int(math.sqrt(math.pow(x - self.center[0], 2) + math.pow(y - self.center[1], 2)))
+        self.radars.append([(x, y), dist])
+    
+    def update(self, game_map):
+        # Set The Speed To 20 For The First Time
+        # Only When Having 4 Output Nodes With Speed Up and Down
+        if not self.speed_set:
+            self.speed = 20
+            self.speed_set = True
+
+        # Get Rotated Sprite And Move Into The Right X-Direction
+        # Don't Let The Car Go Closer Than 20px To The Edge
+        self.rotated_sprite = self.rotate_center(self.sprite, self.angle)
+        self.position[0] += math.cos(math.radians(360 - self.angle)) * self.speed
+        self.position[0] = max(self.position[0], 20)
+        self.position[0] = min(self.position[0], WIDTH - 120)
+
+        # Increase Distance and Time
+        self.distance += self.speed
+        self.time += 1
+        
+        # Same For Y-Position
+        self.position[1] += math.sin(math.radians(360 - self.angle)) * self.speed
+        self.position[1] = max(self.position[1], 20)
+        self.position[1] = min(self.position[1], WIDTH - 120)
+
+        # Calculate New Center
+        self.center = [int(self.position[0]) + CAR_SIZE_X / 2, int(self.position[1]) + CAR_SIZE_Y / 2]
+
+        # Calculate Four Corners
+        # Length Is Half The Side
+        length = 0.5 * CAR_SIZE_X
+        left_top = [self.center[0] + math.cos(math.radians(360 - (self.angle + 30))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 30))) * length]
+        right_top = [self.center[0] + math.cos(math.radians(360 - (self.angle + 150))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 150))) * length]
+        left_bottom = [self.center[0] + math.cos(math.radians(360 - (self.angle + 210))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 210))) * length]
+        right_bottom = [self.center[0] + math.cos(math.radians(360 - (self.angle + 330))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 330))) * length]
+        self.corners = [left_top, right_top, left_bottom, right_bottom]
+
+        # Check Collisions And Clear Radars
+        self.check_collision(game_map)
+        self.radars.clear()
+
+        # From -90 To 120 With Step-Size 45 Check Radar
+        for d in range(-90, 120, 45):
+            self.check_radar(d, game_map)
+
+    def get_data(self):
+        # Get Distances To Border
+        radars = self.radars
+        return_values = [0, 0, 0, 0, 0]
+        for i, radar in enumerate(radars):
+            return_values[i] = int(radar[1] / 30)
+
+        return return_values
+
+    def is_alive(self):
+        # Basic Alive Function
+        return self.alive
+
+    def get_reward(self):
+        # Calculate Reward (Maybe Change?)
+        return self.distance / 50.0
+        # return self.distance / (CAR_SIZE_X / 2)
+
+    def rotate_center(self, image, angle):
+        # Rotate The Rectangle
+        rectangle = image.get_rect()
+        rotated_image = pygame.transform.rotate(image, angle)
+        rotated_rectangle = rectangle.copy()
+        rotated_rectangle.center = rotated_image.get_rect().center
+        rotated_image = rotated_image.subsurface(rotated_rectangle).copy()
+        return rotated_image
+
+
+def run_simulation(genomes, config):
+    
+    # Empty Collections For Nets and Cars
+    nets = []
+    cars = []
+
+    # Initialize PyGame And The Display
+    pygame.init()
+    screen = pygame.display.set_mode((WIDTH, HEIGHT))
+
+    # For All Genomes Passed Create A New Neural Network
+    for i, g in genomes:
+        net = neat.nn.FeedForwardNetwork.create(g, config)
+        nets.append(net)
+        g.fitness = 0
+
+        cars.append(Car())
+
+    # Clock Settings
+    # Font Settings & Loading Map
+    clock = pygame.time.Clock()
+    generation_font = pygame.font.SysFont("Arial", 30)
+    alive_font = pygame.font.SysFont("Arial", 20)
+    game_map = pygame.image.load('map2.png').convert() # Convert Speeds Up A Lot
+    
+    global current_generation
+    current_generation += 1
+
+    # Simple Counter To Roughly Limit Time (Not Good Practice)
+    counter = 0
+
+    while True:
+        # Exit On Quit Event
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                sys.exit(0)
+
+        # For Each Car Get The Acton It Takes
+        for i, car in enumerate(cars):
+            output = nets[i].activate(car.get_data())
+            choice = output.index(max(output))
+            if choice == 0:
+                car.angle += 10 # Left
+            elif choice == 1:
+                car.angle -= 10 # Right
+            elif choice == 2:
+                if(car.speed - 2 >= 12):
+                    car.speed -= 2 # Slow Down
+            else:
+                car.speed += 2 # Speed Up
+        
+        # Check If Car Is Still Alive
+        # Increase Fitness If Yes And Break Loop If Not
+        still_alive = 0
+        for i, car in enumerate(cars):
+            if car.is_alive():
+                still_alive += 1
+                car.update(_map)
+                genomes[i][1].fitness += car.get_reward()
+
+        if still_alive == 0:
+            break
+
+        counter += 1
+        if counter == 30 * 40: # Stop After About 20 Seconds
+            break
+
+        # Draw Map And All Cars That Are Alive
+        screen.blit(game_map, (0, 0))
+        for car in cars:
+            if car.is_alive():
+                car.draw(screen)
+        
+        # Display Info
+        text = generation_font.render("Generation: " + str(current_generation), True, (0,0,0))
+        text_rect = text.get_rect()
+        text_rect.center = (900, 450)
+        screen.blit(text, text_rect)
+
+        text = alive_font.render("Still Alive: " + str(still_alive), True, (0, 0, 0))
+        text_rect = text.get_rect()
+        text_rect.center = (900, 490)
+        screen.blit(text, text_rect)
+
+        pygame.display.flip()
+        clock.tick(30) # 60 FPS
+
+if __name__ == "__main__":
+    import pickle
+    import os
+
+    # Load Config
+    config_path = "config.txt"
+    config = neat.config.Config(neat.DefaultGenome,
+                                neat.DefaultReproduction,
+                                neat.DefaultSpeciesSet,
+                                neat.DefaultStagnation,
+                                config_path)
+
+    # Create Population And Add Reporters
+    population = neat.Population(config)
+    population.add_reporter(neat.StdOutReporter(True))
+    stats = neat.StatisticsReporter()
+    population.add_reporter(stats)
+
+    # Path to store best genome
+    best_genome_path = "best_neat_genome.pkl"
+
+    # Function to save genome
+    def save_best_genome(genome):
+        with open(best_genome_path, "wb") as f:
+            pickle.dump(genome, f)
+        print("\n✅ Best genome saved!\n")
+
+    # Load previous best genome if exists
+    best_fitness_so_far = -1
+    if os.path.exists(best_genome_path):
+        with open(best_genome_path, "rb") as f:
+            old_best = pickle.load(f)
+            best_fitness_so_far = old_best.fitness if hasattr(old_best, "fitness") else -1
+        print(f"\n📂 Loaded previous best fitness: {best_fitness_so_far}\n")
+
+    # Run NEAT algorithm
+    def eval_genomes(genomes, config):
+        global best_fitness_so_far
+        run_simulation(genomes, config)
+        for genome_id, genome in genomes:
+            if genome.fitness > best_fitness_so_far:
+                best_fitness_so_far = genome.fitness
+                save_best_genome(genome)
+
+    # Run NEAT with our eval function
+    population.run(eval_genomes, 1000)

fyp1/requirements.txt

@@ -0,0 +1,2 @@
+neat-python
+pygame

gui/.dockerignore

@@ -0,0 +1,9 @@
+.git
+.gitignore
+.dockerignore
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.env
+db.sqlite3

gui/.gitignore

@@ -0,0 +1,9 @@
+# Environment variables
+.env
+
+# Django
+db.sqlite3
+
+# Python
+__pycache__/
+*.pyc

gui/Dockerfile

@@ -0,0 +1,23 @@
+# Use an official Python runtime as a parent image
+FROM python:3.12
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install any needed packages specified in requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application's code into the container
+COPY . .
+
+# Collect static files
+RUN python manage.py collectstatic --no-input
+
+# Expose the port the app runs on
+EXPOSE 8000
+
+# Run the application
+CMD ["gunicorn", "--bind", "0.0.0.0:8000", "gui.wsgi"]

gui/gui/asgi.py

@@ -0,0 +1,16 @@
+"""
+ASGI config for gui project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.1/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gui.settings')
+
+application = get_asgi_application()

gui/gui/settings.py

@@ -0,0 +1,124 @@
+"""
+Django settings for gui project.
+
+Generated by 'django-admin startproject' using Django 5.1.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.1/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/5.1/ref/settings/
+"""
+
+from pathlib import Path
+from decouple import config
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/5.1/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = config('SECRET_KEY')
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = config('DEBUG', default=False, cast=bool)
+
+ALLOWED_HOSTS = ['*']
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+    'interface',  # Your app
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+    'whitenoise.middleware.WhiteNoiseMiddleware',
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'gui.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [BASE_DIR / 'templates'],  # Add your templates directory here
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'gui.wsgi.application'
+
+# Database
+# https://docs.djangoproject.com/en/5.1/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': BASE_DIR / 'db.sqlite3',
+    }
+}
+
+# Password validation
+# https://docs.djangoproject.com/en/5.1/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+# Internationalization
+# https://docs.djangoproject.com/en/5.1/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_TZ = True
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/5.1/howto/static-files/
+
+STATIC_URL = '/static/'  # Change to include a leading slash
+STATICFILES_DIRS = [BASE_DIR / 'static']  # Ensure Django knows where to find static files
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/5.1/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
+
+STATIC_ROOT = BASE_DIR / 'staticfiles'

gui/gui/urls.py

@@ -0,0 +1,28 @@
+"""
+URL configuration for gui project.
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/5.1/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.urls import path
+from interface.views import index, about, contact,run_neat_simulation,run_ddqn_simulation
+
+urlpatterns = [
+    path('admin/', admin.site.urls),
+    path('', index, name='index'),
+    path('about/', about, name='about'),
+    path('contact/', contact, name='contact'),
+     path('run-neat/', run_neat_simulation, name='run_neat'),
+     path('run-ddqn/', run_ddqn_simulation, name='run_ddqn'),
+]

gui/gui/wsgi.py

@@ -0,0 +1,16 @@
+"""
+WSGI config for gui project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/5.1/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'gui.settings')
+
+application = get_wsgi_application()

gui/interface/admin.py

@@ -0,0 +1,3 @@
+from django.contrib import admin
+
+# Register your models here.

gui/interface/apps.py

@@ -0,0 +1,6 @@
+from django.apps import AppConfig
+
+
+class InterfaceConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'interface'

gui/interface/models.py

@@ -0,0 +1,3 @@
+from django.db import models
+
+# Create your models here.

gui/interface/simulations/ddqn_simulation.py

@@ -0,0 +1,70 @@
+import os
+import numpy as np
+import pygame
+import tensorflow as tf
+from .fyp_simulation import GameEnv
+from .fyp_simulation.ddqn_keras import DDQNAgent
+
+# model_path = "ddqn_model.keras"
+model_path = os.path.join(os.path.dirname(__file__), 'fyp_simulation', 'ddqn_model.keras')
+
+try:
+    model = tf.keras.models.load_model(model_path)
+    print("Model loaded successfully!")
+except Exception as e:
+    print(f" Failed to load model: {e}")
+
+TOTAL_GAMETIME = 10000
+N_EPISODES = 10000
+REPLACE_TARGET = 10
+
+game = GameEnv.RacingEnv()
+game.fps = 60
+
+ddqn_agent = DDQNAgent(alpha=0.0005, gamma=0.99, n_actions=5, epsilon=0.02, 
+                        epsilon_end=0.01, epsilon_dec=0.999, replace_target=REPLACE_TARGET, 
+                        batch_size=64, input_dims=19, fname=model_path)
+
+ddqn_agent.load_model()
+ddqn_agent.update_network_parameters()
+
+def run():
+    for e in range(N_EPISODES):
+        game.reset()
+        done = False
+        score = 0
+        counter = 0
+        gtime = 0
+        observation_, reward, done = game.step(0)
+        observation = np.array(observation_)
+
+        while not done:
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    return
+
+            action = ddqn_agent.choose_action(observation)
+            observation_, reward, done = game.step(action)
+            observation_ = np.array(observation_)
+
+            if reward == 0:
+                counter += 1
+                if counter > 100:
+                    done = True
+            else:
+                counter = 0
+
+            score += reward
+            observation = observation_
+            gtime += 1
+            if gtime >= TOTAL_GAMETIME:
+                done = True
+            game.render(action)
+            #  if score > best_score:
+            # best_score = score
+            # ddqn_agent.save_model()
+            # print(f" New best score {best_score}! Model saved.")
+
+        print(f"Episode: {e}, Reward: {score}")
+
+run()

gui/interface/simulations/fyp1_simulation/best_neat_genome.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:166b4658e68cab4f72d9ea7c248081aa13ac34d447a2df4bfe26fbed975c5274
+size 1199

gui/interface/simulations/fyp1_simulation/config.txt

@@ -0,0 +1,79 @@
+[NEAT]
+fitness_criterion     = max
+fitness_threshold     = 100000000
+pop_size              = 30
+reset_on_extinction   = True
+
+[DefaultGenome]
+# node activation options
+activation_default      = tanh
+activation_mutate_rate  = 0.01
+activation_options      = tanh
+
+# node aggregation options
+aggregation_default     = sum
+aggregation_mutate_rate = 0.01
+aggregation_options     = sum
+
+# node bias options
+bias_init_mean          = 0.0
+bias_init_stdev         = 1.0
+bias_max_value          = 30.0
+bias_min_value          = -30.0
+bias_mutate_power       = 0.5
+bias_mutate_rate        = 0.7
+bias_replace_rate       = 0.1
+
+# genome compatibility options
+compatibility_disjoint_coefficient = 1.0
+compatibility_weight_coefficient   = 0.5
+
+# connection add/remove rates
+conn_add_prob           = 0.5
+conn_delete_prob        = 0.5
+
+# connection enable options
+enabled_default         = True
+enabled_mutate_rate     = 0.01
+
+feed_forward            = True
+initial_connection      = full
+
+# node add/remove rates
+node_add_prob           = 0.2
+node_delete_prob        = 0.2
+
+# network parameters
+num_hidden              = 0
+num_inputs              = 5
+num_outputs             = 4
+
+# node response options
+response_init_mean      = 1.0
+response_init_stdev     = 0.0
+response_max_value      = 30.0
+response_min_value      = -30.0
+response_mutate_power   = 0.0
+response_mutate_rate    = 0.0
+response_replace_rate   = 0.0
+
+# connection weight options
+weight_init_mean        = 0.0
+weight_init_stdev       = 1.0
+weight_max_value        = 30
+weight_min_value        = -30
+weight_mutate_power     = 0.5
+weight_mutate_rate      = 0.8
+weight_replace_rate     = 0.1
+
+[DefaultSpeciesSet]
+compatibility_threshold = 2.0
+
+[DefaultStagnation]
+species_fitness_func = max
+max_stagnation       = 20
+species_elitism      = 2
+
+[DefaultReproduction]
+elitism            = 3
+survival_threshold = 0.2

gui/interface/simulations/fyp1_simulation/newcar.py

@@ -0,0 +1,288 @@
+import math
+import random
+import sys
+import os
+
+import neat
+import pygame
+
+# Constants
+#WIDTH = 1500
+#HEIGHT = 800
+
+WIDTH = 1920
+HEIGHT = 1080
+
+
+
+CAR_SIZE_X = 60    
+CAR_SIZE_Y = 60
+
+BORDER_COLOR = (255, 255, 255, 255) # Color To Crash on Hit
+
+current_generation = 0 # Generation counter
+
+class Car:
+
+    def __init__(self):
+        # Load Car Sprite and Rotate
+        self.sprite = pygame.image.load('car.png').convert() # Convert Speeds Up A Lot
+        self.sprite = pygame.transform.scale(self.sprite, (CAR_SIZE_X, CAR_SIZE_Y))
+        self.rotated_sprite = self.sprite 
+
+        # self.position = [690, 740] # Starting Position
+        self.position = [830, 920] # Starting Position
+        self.angle = 0
+        self.speed = 0
+
+        self.speed_set = False # Flag For Default Speed Later on
+
+        self.center = [self.position[0] + CAR_SIZE_X / 2, self.position[1] + CAR_SIZE_Y / 2] # Calculate Center
+
+        self.radars = [] # List For Sensors / Radars
+        self.drawing_radars = [] # Radars To Be Drawn
+
+        self.alive = True # Boolean To Check If Car is Crashed
+
+        self.distance = 0 # Distance Driven
+        self.time = 0 # Time Passed
+
+    def draw(self, screen):
+        screen.blit(self.rotated_sprite, self.position) # Draw Sprite
+        self.draw_radar(screen) #OPTIONAL FOR SENSORS
+
+    def draw_radar(self, screen):
+        # Optionally Draw All Sensors / Radars
+        for radar in self.radars:
+            position = radar[0]
+            pygame.draw.line(screen, (0, 255, 0), self.center, position, 1)
+            pygame.draw.circle(screen, (0, 255, 0), position, 5)
+
+    def check_collision(self, game_map):
+        self.alive = True
+        for point in self.corners:
+            # If Any Corner Touches Border Color -> Crash
+            # Assumes Rectangle
+            if game_map.get_at((int(point[0]), int(point[1]))) == BORDER_COLOR:
+                self.alive = False
+                break
+
+    def check_radar(self, degree, game_map):
+        length = 0
+        x = int(self.center[0] + math.cos(math.radians(360 - (self.angle + degree))) * length)
+        y = int(self.center[1] + math.sin(math.radians(360 - (self.angle + degree))) * length)
+
+        # While We Don't Hit BORDER_COLOR AND length < 300 (just a max) -> go further and further
+        while not game_map.get_at((x, y)) == BORDER_COLOR and length < 300:
+            length = length + 1
+            x = int(self.center[0] + math.cos(math.radians(360 - (self.angle + degree))) * length)
+            y = int(self.center[1] + math.sin(math.radians(360 - (self.angle + degree))) * length)
+
+        # Calculate Distance To Border And Append To Radars List
+        dist = int(math.sqrt(math.pow(x - self.center[0], 2) + math.pow(y - self.center[1], 2)))
+        self.radars.append([(x, y), dist])
+    
+    def update(self, game_map):
+        # Set The Speed To 20 For The First Time
+        # Only When Having 4 Output Nodes With Speed Up and Down
+        if not self.speed_set:
+            self.speed = 20
+            self.speed_set = True
+
+        # Get Rotated Sprite And Move Into The Right X-Direction
+        # Don't Let The Car Go Closer Than 20px To The Edge
+        self.rotated_sprite = self.rotate_center(self.sprite, self.angle)
+        self.position[0] += math.cos(math.radians(360 - self.angle)) * self.speed
+        self.position[0] = max(self.position[0], 20)
+        self.position[0] = min(self.position[0], WIDTH - 120)
+
+        # Increase Distance and Time
+        self.distance += self.speed
+        self.time += 1
+        
+        # Same For Y-Position
+        self.position[1] += math.sin(math.radians(360 - self.angle)) * self.speed
+        self.position[1] = max(self.position[1], 20)
+        self.position[1] = min(self.position[1], WIDTH - 120)
+
+        # Calculate New Center
+        self.center = [int(self.position[0]) + CAR_SIZE_X / 2, int(self.position[1]) + CAR_SIZE_Y / 2]
+
+        # Calculate Four Corners
+        # Length Is Half The Side
+        length = 0.5 * CAR_SIZE_X
+        left_top = [self.center[0] + math.cos(math.radians(360 - (self.angle + 30))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 30))) * length]
+        right_top = [self.center[0] + math.cos(math.radians(360 - (self.angle + 150))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 150))) * length]
+        left_bottom = [self.center[0] + math.cos(math.radians(360 - (self.angle + 210))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 210))) * length]
+        right_bottom = [self.center[0] + math.cos(math.radians(360 - (self.angle + 330))) * length, self.center[1] + math.sin(math.radians(360 - (self.angle + 330))) * length]
+        self.corners = [left_top, right_top, left_bottom, right_bottom]
+
+        # Check Collisions And Clear Radars
+        self.check_collision(game_map)
+        self.radars.clear()
+
+        # From -90 To 120 With Step-Size 45 Check Radar
+        for d in range(-90, 120, 45):
+            self.check_radar(d, game_map)
+
+    def get_data(self):
+        # Get Distances To Border
+        radars = self.radars
+        return_values = [0, 0, 0, 0, 0]
+        for i, radar in enumerate(radars):
+            return_values[i] = int(radar[1] / 30)
+
+        return return_values
+
+    def is_alive(self):
+        # Basic Alive Function
+        return self.alive
+
+    def get_reward(self):
+        # Calculate Reward (Maybe Change?)
+        return self.distance / 50.0
+        # return self.distance / (CAR_SIZE_X / 2)
+
+    def rotate_center(self, image, angle):
+        # Rotate The Rectangle
+        rectangle = image.get_rect()
+        rotated_image = pygame.transform.rotate(image, angle)
+        rotated_rectangle = rectangle.copy()
+        rotated_rectangle.center = rotated_image.get_rect().center
+        rotated_image = rotated_image.subsurface(rotated_rectangle).copy()
+        return rotated_image
+
+
+def run_simulation(genomes, config):
+    
+    # Empty Collections For Nets and Cars
+    nets = []
+    cars = []
+
+    # Initialize PyGame And The Display
+    pygame.init()
+    screen = pygame.display.set_mode((WIDTH, HEIGHT))
+
+    # For All Genomes Passed Create A New Neural Network
+    for i, g in genomes:
+        net = neat.nn.FeedForwardNetwork.create(g, config)
+        nets.append(net)
+        g.fitness = 0
+
+        cars.append(Car())
+
+    # Clock Settings
+    # Font Settings & Loading Map
+    clock = pygame.time.Clock()
+    generation_font = pygame.font.SysFont("Arial", 30)
+    alive_font = pygame.font.SysFont("Arial", 20)
+    game_map = pygame.image.load('map2.png').convert() # Convert Speeds Up A Lot
+    
+    global current_generation
+    current_generation += 1
+
+    # Simple Counter To Roughly Limit Time (Not Good Practice)
+    counter = 0
+
+    while True:
+        # Exit On Quit Event
+        for event in pygame.event.get():
+            if event.type == pygame.QUIT:
+                sys.exit(0)
+
+        # For Each Car Get The Acton It Takes
+        for i, car in enumerate(cars):
+            output = nets[i].activate(car.get_data())
+            choice = output.index(max(output))
+            if choice == 0:
+                car.angle += 10 # Left
+            elif choice == 1:
+                car.angle -= 10 # Right
+            elif choice == 2:
+                if(car.speed - 2 >= 12):
+                    car.speed -= 2 # Slow Down
+            else:
+                car.speed += 2 # Speed Up
+        
+        # Check If Car Is Still Alive
+        # Increase Fitness If Yes And Break Loop If Not
+        still_alive = 0
+        for i, car in enumerate(cars):
+            if car.is_alive():
+                still_alive += 1
+                car.update(game_map)
+                genomes[i][1].fitness += car.get_reward()
+
+        if still_alive == 0:
+            break
+
+        counter += 1
+        if counter == 30 * 40: # Stop After About 20 Seconds
+            break
+
+        # Draw Map And All Cars That Are Alive
+        screen.blit(game_map, (0, 0))
+        for car in cars:
+            if car.is_alive():
+                car.draw(screen)
+        
+        # Display Info
+        text = generation_font.render("Generation: " + str(current_generation), True, (0,0,0))
+        text_rect = text.get_rect()
+        text_rect.center = (900, 450)
+        screen.blit(text, text_rect)
+
+        text = alive_font.render("Still Alive: " + str(still_alive), True, (0, 0, 0))
+        text_rect = text.get_rect()
+        text_rect.center = (900, 490)
+        screen.blit(text, text_rect)
+
+        pygame.display.flip()
+        clock.tick(30) # 60 FPS
+
+if __name__ == "__main__":
+    import pickle
+    import os
+
+    # Load Config
+    config_path = "config.txt"
+    config = neat.config.Config(neat.DefaultGenome,
+                                neat.DefaultReproduction,
+                                neat.DefaultSpeciesSet,
+                                neat.DefaultStagnation,
+                                config_path)
+
+    # Create Population And Add Reporters
+    population = neat.Population(config)
+    population.add_reporter(neat.StdOutReporter(True))
+    stats = neat.StatisticsReporter()
+    population.add_reporter(stats)
+
+    # Path to store best genome
+    best_genome_path = "best_neat_genome.pkl"
+
+    # Function to save genome
+    def save_best_genome(genome):
+        with open(best_genome_path, "wb") as f:
+            pickle.dump(genome, f)
+        print("\n✅ Best genome saved!\n")
+
+    # Load previous best genome if exists
+    best_fitness_so_far = -1
+    if os.path.exists(best_genome_path):
+        with open(best_genome_path, "rb") as f:
+            old_best = pickle.load(f)
+            best_fitness_so_far = old_best.fitness if hasattr(old_best, "fitness") else -1
+        print(f"\n📂 Loaded previous best fitness: {best_fitness_so_far}\n")
+
+    # Run NEAT algorithm
+    def eval_genomes(genomes, config):
+        global best_fitness_so_far
+        run_simulation(genomes, config)
+        for genome_id, genome in genomes:
+            if genome.fitness > best_fitness_so_far:
+                best_fitness_so_far = genome.fitness
+                save_best_genome(genome)
+
+    # Run NEAT with our eval function
+    population.run(eval_genomes, 1000)

gui/interface/simulations/fyp_simulation/GameEnv.py

@@ -0,0 +1,512 @@
+import pygame
+import math
+from .Walls import Wall
+from .Walls import getWalls
+from .Goals import Goal
+from .Goals import getGoals
+
+
+
+GOALREWARD = 1
+LIFE_REWARD = 0
+PENALTY = -1
+
+
+def distance(pt1, pt2):
+    return(((pt1.x - pt2.x)**2 + (pt1.y - pt2.y)**2)**0.5)
+
+def rotate(origin,point,angle):
+    qx = origin.x + math.cos(angle) * (point.x - origin.x) - math.sin(angle) * (point.y - origin.y)
+    qy = origin.y + math.sin(angle) * (point.x - origin.x) + math.cos(angle) * (point.y - origin.y)
+    q = myPoint(qx, qy)
+    return q
+
+def rotateRect(pt1, pt2, pt3, pt4, angle):
+
+    pt_center = myPoint((pt1.x + pt3.x)/2, (pt1.y + pt3.y)/2)
+
+    pt1 = rotate(pt_center,pt1,angle)
+    pt2 = rotate(pt_center,pt2,angle)
+    pt3 = rotate(pt_center,pt3,angle)
+    pt4 = rotate(pt_center,pt4,angle)
+
+    return pt1, pt2, pt3, pt4
+
+class myPoint:
+    def __init__(self, x, y):
+        self.x = x
+        self.y = y
+        
+class myLine:
+    def __init__(self, pt1, pt2):
+        self.pt1 = myPoint(pt1.x, pt1.y)
+        self.pt2 = myPoint(pt2.x, pt2.y)
+
+class Ray:
+    def __init__(self,x,y,angle):
+        self.x = x
+        self.y = y
+        self.angle = angle
+
+    def cast(self, wall):
+        x1 = wall.x1 
+        y1 = wall.y1
+        x2 = wall.x2
+        y2 = wall.y2
+
+        vec = rotate(myPoint(0,0), myPoint(0,-1000), self.angle)
+        
+        x3 = self.x
+        y3 = self.y
+        x4 = self.x + vec.x
+        y4 = self.y + vec.y
+
+        den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+            
+        if(den == 0):
+            den = 0
+        else:
+            t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / den
+            u = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den
+
+            if t > 0 and t < 1 and u < 1 and u > 0:
+                pt = myPoint(math.floor(x1 + t * (x2 - x1)), math.floor(y1 + t * (y2 - y1)))
+                return(pt)
+
+ 
+
+class Car:
+    def __init__(self, x, y):
+        self.pt = myPoint(x, y)
+        self.x = x
+        self.y = y
+        self.width = 14
+        self.height = 30
+
+        self.points = 0
+
+        self.original_image = pygame.image.load("car.png").convert()
+        self.image = self.original_image  # This will reference the rotated image.
+        self.image.set_colorkey((0,0,0))
+        self.rect = self.image.get_rect().move(self.x, self.y)
+
+        self.angle = math.radians(180)
+        self.soll_angle = self.angle
+
+        self.dvel = 1
+        self.vel = 0
+        self.velX = 0
+        self.velY = 0
+        self.maxvel = 15 # before 15
+
+        self.angle = math.radians(180)
+        self.soll_angle = self.angle
+
+        self.pt1 = myPoint(self.pt.x - self.width / 2, self.pt.y - self.height / 2)
+        self.pt2 = myPoint(self.pt.x + self.width / 2, self.pt.y - self.height / 2)
+        self.pt3 = myPoint(self.pt.x + self.width / 2, self.pt.y + self.height / 2)
+        self.pt4 = myPoint(self.pt.x - self.width / 2, self.pt.y + self.height / 2)
+
+        self.p1 = self.pt1
+        self.p2 = self.pt2
+        self.p3 = self.pt3
+        self.p4 = self.pt4
+
+        self.distances = []
+    
+
+    def action(self, choice):
+        if choice == 0:
+            pass
+        elif choice == 1:
+            self.accelerate(self.dvel)
+        elif choice == 8:
+            self.accelerate(self.dvel)
+            self.turn(1)
+        elif choice == 7:
+            self.accelerate(self.dvel)
+            self.turn(-1)
+        elif choice == 4:
+            self.accelerate(-self.dvel)
+        elif choice == 5:
+            self.accelerate(-self.dvel)
+            self.turn(1)
+        elif choice == 6:
+            self.accelerate(-self.dvel)
+            self.turn(-1)
+        elif choice == 3:
+            self.turn(1)
+        elif choice == 2:
+            self.turn(-1)
+        pass
+    
+    def accelerate(self,dvel):
+        dvel = dvel * 2
+
+        self.vel = self.vel + dvel
+
+        if self.vel > self.maxvel:
+            self.vel = self.maxvel
+        
+        if self.vel < -self.maxvel:
+            self.vel = -self.maxvel
+        
+        
+    def turn(self, dir):
+        self.soll_angle = self.soll_angle + dir * math.radians(15)
+    
+    def update(self):
+
+        #drifting code         
+        self.angle = self.soll_angle
+
+        vec_temp = rotate(myPoint(0,0), myPoint(0,self.vel), self.angle)
+        self.velX, self.velY = vec_temp.x, vec_temp.y
+
+        self.x = self.x + self.velX
+        self.y = self.y + self.velY
+
+        self.rect.center = self.x, self.y
+
+        self.pt1 = myPoint(self.pt1.x + self.velX, self.pt1.y + self.velY)
+        self.pt2 = myPoint(self.pt2.x + self.velX, self.pt2.y + self.velY)
+        self.pt3 = myPoint(self.pt3.x + self.velX, self.pt3.y + self.velY)
+        self.pt4 = myPoint(self.pt4.x + self.velX, self.pt4.y + self.velY)
+
+        self.p1 ,self.p2 ,self.p3 ,self.p4  = rotateRect(self.pt1, self.pt2, self.pt3, self.pt4, self.soll_angle)
+
+        self.image = pygame.transform.rotate(self.original_image, 90 - self.soll_angle * 180 / math.pi)
+        x, y = self.rect.center  # Save its current center.
+        self.rect = self.image.get_rect()  # Replace old rect with new rect.
+        self.rect.center = (x, y)
+
+    def cast(self, walls):
+
+        ray1 = Ray(self.x, self.y, self.soll_angle)
+        ray2 = Ray(self.x, self.y, self.soll_angle - math.radians(30))
+        ray3 = Ray(self.x, self.y, self.soll_angle + math.radians(30))
+        ray4 = Ray(self.x, self.y, self.soll_angle + math.radians(45))
+        ray5 = Ray(self.x, self.y, self.soll_angle - math.radians(45))
+        ray6 = Ray(self.x, self.y, self.soll_angle + math.radians(90))
+        ray7 = Ray(self.x, self.y, self.soll_angle - math.radians(90))
+        ray8 = Ray(self.x, self.y, self.soll_angle + math.radians(180))
+
+        ray9 = Ray(self.x, self.y, self.soll_angle + math.radians(10))
+        ray10 = Ray(self.x, self.y, self.soll_angle - math.radians(10))
+        ray11 = Ray(self.x, self.y, self.soll_angle + math.radians(135))
+        ray12 = Ray(self.x, self.y, self.soll_angle - math.radians(135))
+        ray13 = Ray(self.x, self.y, self.soll_angle + math.radians(20))
+        ray14 = Ray(self.x, self.y, self.soll_angle - math.radians(20))
+
+        ray15 = Ray(self.p1.x,self.p1.y, self.soll_angle + math.radians(90))
+        ray16 = Ray(self.p2.x,self.p2.y, self.soll_angle - math.radians(90))
+
+        ray17 = Ray(self.p1.x,self.p1.y, self.soll_angle + math.radians(0))
+        ray18 = Ray(self.p2.x,self.p2.y, self.soll_angle - math.radians(0))
+
+        self.rays = []
+        self.rays.append(ray1)
+        self.rays.append(ray2)
+        self.rays.append(ray3)
+        self.rays.append(ray4)
+        self.rays.append(ray5)
+        self.rays.append(ray6)
+        self.rays.append(ray7)
+        self.rays.append(ray8)
+
+        self.rays.append(ray9)
+        self.rays.append(ray10)
+        self.rays.append(ray11)
+        self.rays.append(ray12)
+        self.rays.append(ray13)
+        self.rays.append(ray14)
+
+        self.rays.append(ray15)
+        self.rays.append(ray16)
+
+        self.rays.append(ray17)
+        self.rays.append(ray18)
+
+
+        observations = []
+        self.closestRays = []
+
+        for ray in self.rays:
+            closest = None #myPoint(0,0)
+            record = math.inf
+            for wall in walls:
+                pt = ray.cast(wall)
+                if pt:
+                    dist = distance(myPoint(self.x, self.y),pt)
+                    if dist < record:
+                        record = dist
+                        closest = pt
+
+            if closest: 
+                #append distance for current ray 
+                self.closestRays.append(closest)
+                observations.append(record)
+               
+            else:
+                observations.append(1000)
+
+        for i in range(len(observations)):
+            #invert observation values 0 is far away 1 is close
+            observations[i] = ((1000 - observations[i]) / 1000)
+
+        observations.append(self.vel / self.maxvel)
+        return observations
+
+    def collision(self, wall):
+
+        line1 = myLine(self.p1, self.p2)
+        line2 = myLine(self.p2, self.p3)
+        line3 = myLine(self.p3, self.p4)
+        line4 = myLine(self.p4, self.p1)
+
+        x1 = wall.x1 
+        y1 = wall.y1
+        x2 = wall.x2
+        y2 = wall.y2
+
+        lines = []
+        lines.append(line1)
+        lines.append(line2)
+        lines.append(line3)
+        lines.append(line4)
+
+        for li in lines:
+            
+            x3 = li.pt1.x
+            y3 = li.pt1.y
+            x4 = li.pt2.x
+            y4 = li.pt2.y
+
+            den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+            
+            if(den == 0):
+                den = 0
+            else:
+                t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / den
+                u = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den
+
+                if t > 0 and t < 1 and u < 1 and u > 0:
+                    return(True)
+        
+        return(False)
+    
+    def score(self, goal):
+        
+        line1 = myLine(self.p1, self.p3)
+
+        vec = rotate(myPoint(0,0), myPoint(0,-50), self.angle)
+        line1 = myLine(myPoint(self.x,self.y),myPoint(self.x + vec.x, self.y + vec.y))
+
+        x1 = goal.x1 
+        y1 = goal.y1
+        x2 = goal.x2
+        y2 = goal.y2
+            
+        x3 = line1.pt1.x
+        y3 = line1.pt1.y
+        x4 = line1.pt2.x
+        y4 = line1.pt2.y
+
+        den = (x1 - x2) * (y3 - y4) - (y1 - y2) * (x3 - x4)
+        
+        if(den == 0):
+            den = 0
+        else:
+            t = ((x1 - x3) * (y3 - y4) - (y1 - y3) * (x3 - x4)) / den
+            u = -((x1 - x2) * (y1 - y3) - (y1 - y2) * (x1 - x3)) / den
+
+            if t > 0 and t < 1 and u < 1 and u > 0:
+                pt = math.floor(x1 + t * (x2 - x1)), math.floor(y1 + t * (y2 - y1))
+
+                d = distance(myPoint(self.x, self.y), myPoint(pt[0], pt[1]))
+                if d < 20:
+                    #pygame.draw.circle(win, (0,255,0), pt, 5)
+                    self.points += GOALREWARD
+                    return(True)
+
+        return(False)
+
+    def reset(self):
+
+        self.x = 50
+        self.y = 300
+        self.velX = 0
+        self.velY = 0
+        self.vel = 0
+        self.angle = math.radians(180)
+        self.soll_angle = self.angle
+        self.points = 0
+
+        self.pt1 = myPoint(self.pt.x - self.width / 2, self.pt.y - self.height / 2)
+        self.pt2 = myPoint(self.pt.x + self.width / 2, self.pt.y - self.height / 2)
+        self.pt3 = myPoint(self.pt.x + self.width / 2, self.pt.y + self.height / 2)
+        self.pt4 = myPoint(self.pt.x - self.width / 2, self.pt.y + self.height / 2)
+
+        self.p1 = self.pt1
+        self.p2 = self.pt2
+        self.p3 = self.pt3
+        self.p4 = self.pt4
+
+    def draw(self, win):
+        win.blit(self.image, self.rect)
+  
+
+class RacingEnv:
+
+    def __init__(self):
+        pygame.init()
+        self.font = pygame.font.Font(pygame.font.get_default_font(), 36)
+
+        self.fps = 120
+        self.width = 1000
+        self.height = 600
+        self.history = []
+
+        self.screen = pygame.display.set_mode((self.width, self.height))
+        pygame.display.set_caption("IMS using DDQN")
+        self.screen.fill((0,0,0))
+        self.back_image = pygame.image.load("track.png").convert()
+        self.back_rect = self.back_image.get_rect().move(0, 0)
+        self.action_space = None
+        self.observation_space = None
+        self.game_reward = 0
+        self.score = 0
+ 
+        self.reset()
+
+
+    def reset(self):
+        self.screen.fill((0, 0, 0))
+
+        self.car = Car(50, 300)
+        self.walls = getWalls()
+        self.goals = getGoals()
+        self.game_reward = 0
+
+    def step(self, action):
+
+        done = False
+        self.car.action(action)
+        self.car.update()
+        reward = LIFE_REWARD
+
+        # Check if car passes Goal and scores
+        index = 1
+        for goal in self.goals:
+            
+            if index > len(self.goals):
+                index = 1
+            if goal.isactiv:
+                if self.car.score(goal):
+                    goal.isactiv = False
+                    self.goals[index-2].isactiv = True
+                    reward += GOALREWARD
+
+            index = index + 1
+
+        #check if car crashed in the wall
+        for wall in self.walls:
+            if self.car.collision(wall):
+                reward += PENALTY
+                done = True
+
+        new_state = self.car.cast(self.walls)
+        #normalize states
+        if done:
+            new_state = None
+
+        return new_state, reward, done
+
+    def render(self, action):
+
+        DRAW_WALLS = True
+        DRAW_GOALS = True
+        DRAW_RAYS = True
+
+        pygame.time.delay(10)
+
+        self.clock = pygame.time.Clock()
+        self.screen.fill((0, 0, 0))
+
+        self.screen.blit(self.back_image, self.back_rect)
+        # Draw checkered start line (black and white boxes)
+        start_x = 50  # same as Car's starting x
+        start_y = 300  # same as Car's starting y
+        line_width = 80
+        line_height = 20
+        num_squares = 16  # Number of squares in the checkered line
+        square_width = line_width // num_squares
+        for i in range(num_squares):
+            color = (255, 255, 255) if i % 2 == 0 else (0, 0, 0)
+            rect_x = start_x - line_width // 2 + i * square_width
+            pygame.draw.rect(self.screen, color, (rect_x, start_y - line_height // 2, square_width, line_height))
+        if DRAW_WALLS:
+            for wall in self.walls:
+                wall.draw(self.screen)
+        
+        if DRAW_GOALS:
+            for goal in self.goals:
+                goal.draw(self.screen)
+                if goal.isactiv:
+                    goal.draw(self.screen)
+        
+        self.car.draw(self.screen)
+
+        if DRAW_RAYS:
+            i = 0
+            for pt in self.car.closestRays:
+                pygame.draw.circle(self.screen, (0,0,255), (pt.x, pt.y), 5)
+                i += 1
+                if i < 15:
+                    pygame.draw.line(self.screen, (255,255,255), (self.car.x, self.car.y), (pt.x, pt.y), 1)
+                elif i >=15 and i < 17:
+                    pygame.draw.line(self.screen, (255,255,255), ((self.car.p1.x + self.car.p2.x)/2, (self.car.p1.y + self.car.p2.y)/2), (pt.x, pt.y), 1)
+                elif i == 17:
+                    pygame.draw.line(self.screen, (255,255,255), (self.car.p1.x , self.car.p1.y ), (pt.x, pt.y), 1)
+                else:
+                    pygame.draw.line(self.screen, (255,255,255), (self.car.p2.x, self.car.p2.y), (pt.x, pt.y), 1)
+
+        #render controll
+        pygame.draw.rect(self.screen,(255,255,255),(800, 100, 40, 40),2)
+        pygame.draw.rect(self.screen,(255,255,255),(850, 100, 40, 40),2)
+        pygame.draw.rect(self.screen,(255,255,255),(900, 100, 40, 40),2)
+        pygame.draw.rect(self.screen,(255,255,255),(850, 50, 40, 40),2)
+
+        if action == 4:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 50, 40, 40)) 
+        elif action == 6:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 50, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(800, 100, 40, 40))
+        elif action == 5:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 50, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(900, 100, 40, 40))
+        elif action == 1:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 100, 40, 40)) 
+        elif action == 8:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 100, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(800, 100, 40, 40))
+        elif action == 7:
+            pygame.draw.rect(self.screen,(0,255,0),(850, 100, 40, 40))
+            pygame.draw.rect(self.screen,(0,255,0),(900, 100, 40, 40))
+        elif action == 2:
+            pygame.draw.rect(self.screen,(0,255,0),(800, 100, 40, 40))
+        elif action == 3:
+            pygame.draw.rect(self.screen,(0,255,0),(900, 100, 40, 40))
+
+        # score
+        text_surface = self.font.render(f'Points {self.car.points}', True, pygame.Color('green'))
+        self.screen.blit(text_surface, dest=(0, 0))
+        # speed
+        text_surface = self.font.render(f'Speed {self.car.vel*-1}', True, pygame.Color('green'))
+        self.screen.blit(text_surface, dest=(800, 0))
+
+        self.clock.tick(self.fps)
+        pygame.display.update()
+
+    def close(self):
+        pygame.quit()

gui/interface/simulations/fyp_simulation/Goals.py

@@ -0,0 +1,99 @@
+import pygame
+
+class Goal:
+    def __init__(self, x1, y1, x2, y2):
+        self.x1 = x1
+        self.y1 = y1
+        self.x2 = x2
+        self.y2 = y2
+
+        self.isactiv = False
+    
+    def draw(self, win):
+        pygame.draw.line(win, (0,255,0), (self.x1, self.y1), (self.x2, self.y2), 2)
+        if self.isactiv:
+            pygame.draw.line(win, (255,0,0), (self.x1, self.y1), (self.x2, self.y2), 2)
+
+# the file of shame
+def getGoals():
+    goals = []
+
+    goal1 = Goal(0,200,120,200)
+    goal2 = Goal(0,100,120,150)
+    goal2_5 = Goal(0,0,150,130)
+    goal3 = Goal(120,0,170,120)
+    goal3_5 = Goal(200,0,200,120)
+    goal4 = Goal(270,0,270,110)
+    goal4_5 = Goal(350,0,350,110)
+    goal5 = Goal(450,0,450,110)
+    goal5_5 = Goal(525,0,525,110)
+    goal6 = Goal(600,0,550,130)
+    goal6_5 = Goal(550,130,700,60)
+    goal7 = Goal(550,130,700,130)
+    goal7_5 = Goal(550,130,650,200)
+    goal8 = Goal(550,130,570,240)
+    goal9 = Goal(410,130,430,260)
+    goal9_5 = Goal(430,260,300,350)
+    goal10 = Goal(430,260,260,260)
+    goal10_5 = Goal(430,260,280,180)
+    goal11 = Goal(430,260,400,400)
+    goal12 = Goal(550,260,570,400)
+    goal13 = Goal(750,400,650,200)
+    goal14 = Goal(750,400,800,160)
+    goal15 = Goal(750,400,950,240)
+    goal16 = Goal(750,400,980,440)
+    goal17 = Goal(750,400,900,600)
+    goal18 = Goal(750,460,750,600)
+    goal19 = Goal(670,460,670,600)
+    goal19_5 = Goal(590,460,590,600)
+    goal20 = Goal(510,460,510,600)
+    goal20_5 = Goal(430,460,430,600)
+    goal21 = Goal(350,460,350,600)
+    goal21_5 = Goal(280,460,278,600)
+    goal22 = Goal(210,460,190,600)
+    goal22_5 = Goal(80,600,175,440)
+    goal23 = Goal(150,420,0,570)
+    goal23_5 = Goal(0,450,130,400)
+    goal24 = Goal(0,380,130,380)
+
+    goals.append(goal1)
+    goals.append(goal2)
+    goals.append(goal2_5)
+    goals.append(goal3)
+    goals.append(goal3_5)
+    goals.append(goal4)
+    goals.append(goal4_5)
+    goals.append(goal5)
+    goals.append(goal5_5)
+    goals.append(goal6)
+    goals.append(goal6_5)
+    goals.append(goal7)
+    goals.append(goal7_5)
+    goals.append(goal8)
+    goals.append(goal9)
+    goals.append(goal10_5)
+    goals.append(goal10)
+    goals.append(goal9_5)
+    goals.append(goal11)
+    goals.append(goal12)
+    goals.append(goal13)
+    goals.append(goal14)
+    goals.append(goal15)
+    goals.append(goal16)
+    goals.append(goal17)
+    goals.append(goal18)
+    goals.append(goal19)
+    goals.append(goal19_5)
+    goals.append(goal20)
+    goals.append(goal20_5)
+    goals.append(goal21)
+    goals.append(goal21_5)
+    goals.append(goal22)
+    goals.append(goal22_5)
+    goals.append(goal23)
+    goals.append(goal23_5)
+    goals.append(goal24)
+
+    goals[len(goals)-1].isactiv = True
+
+    return(goals)

gui/interface/simulations/fyp_simulation/Walls.py

@@ -0,0 +1,113 @@
+import pygame
+
+class Wall:
+    def __init__(self, x1, y1, x2, y2):
+        self.x1 = x1
+        self.y1 = y1
+        self.x2 = x2
+        self.y2 = y2
+    
+    def draw(self, win):
+        pygame.draw.line(win, (255,255,255), (self.x1, self.y1), (self.x2, self.y2), 5)
+
+def getWalls():
+    walls = []
+
+    wall1 = Wall(12, 451, 15, 130)
+    wall2 = Wall(15, 130, 61, 58)
+    wall3 = Wall(61, 58, 149, 14)
+    wall4 = Wall(149, 14, 382, 20)
+    wall5 = Wall(382, 20, 549, 31)
+    wall6 = Wall(549, 31, 636, 58)
+    wall7 = Wall(636, 58, 678, 102)
+    wall8 = Wall(678, 102, 669, 167)
+    wall9 = Wall(669, 167, 600, 206)
+    wall10 = Wall(600, 206, 507, 214)
+    wall11 = Wall(507, 214, 422, 232)
+    wall12 = Wall(422, 232, 375, 263)
+    wall13 = Wall(375, 263, 379, 283)
+    wall14 = Wall(379, 283, 454, 299)
+    wall15 = Wall(454, 299, 613, 286)
+    wall16 = Wall(613, 286, 684, 238)
+    wall17 = Wall(684, 238, 752, 180)
+    wall18 = Wall(752, 180, 862, 185)
+    wall19 = Wall(862, 185, 958, 279)
+    wall20 = Wall(958, 279, 953, 410)
+    wall21 = Wall(953, 410, 925, 505)
+    wall22 = Wall(925, 505, 804, 566)
+    wall23 = Wall(804, 566, 150, 570)
+    wall24 = Wall(150, 570, 46, 529)
+    wall25 = Wall(46, 529, 12, 451)
+    wall27 = Wall(104, 436, 96, 161)
+    wall28 = Wall(96, 161, 122, 122)
+    wall29 = Wall(122, 122, 199, 91)
+    wall30 = Wall(199, 91, 376, 94)
+    wall31 = Wall(376, 94, 469, 100)
+    wall32 = Wall(469, 100, 539, 102)
+    wall33 = Wall(539, 102, 585, 121)
+    wall34 = Wall(585, 121, 585, 139)
+    wall35 = Wall(585, 139, 454, 158)
+    wall36 = Wall(454, 158, 352, 183)
+    wall37 = Wall(352, 183, 293, 239)
+    wall38 = Wall(293, 239, 294, 318)
+    wall39 = Wall(294, 318, 361, 357)
+    wall40 = Wall(361, 357, 490, 373)
+    wall41 = Wall(490, 373, 671, 359)
+    wall42 = Wall(671, 359, 752, 300) #
+    wall43 = Wall(752, 300, 812, 310)#
+    wall44 = Wall(812, 310, 854, 369)
+    wall45 = Wall(854, 369, 854, 429)
+    wall46 = Wall(854, 429, 754, 483)
+    wall47 = Wall(754, 483, 192, 489)
+    wall48 = Wall(192, 489, 104, 436)
+
+    walls.append(wall1)
+    walls.append(wall2)
+    walls.append(wall3)
+    walls.append(wall4)
+    walls.append(wall5)
+    walls.append(wall6)
+    walls.append(wall7)
+    walls.append(wall8)
+    walls.append(wall9)
+    walls.append(wall10)
+    walls.append(wall11)
+    walls.append(wall12)
+    walls.append(wall13)
+    walls.append(wall14)
+    walls.append(wall15)
+    walls.append(wall16)
+    walls.append(wall17)
+    walls.append(wall18)
+    walls.append(wall19)
+    walls.append(wall20)
+    walls.append(wall21)
+    walls.append(wall22)
+    walls.append(wall23)
+    walls.append(wall24)
+    walls.append(wall25)
+
+    walls.append(wall27)
+    walls.append(wall28)
+    walls.append(wall29)
+    walls.append(wall30)
+    walls.append(wall31)
+    walls.append(wall32)
+    walls.append(wall33)
+    walls.append(wall34)
+    walls.append(wall35)
+    walls.append(wall36)
+    walls.append(wall37)
+    walls.append(wall38)
+    walls.append(wall39)
+    walls.append(wall40)
+    walls.append(wall41)
+    walls.append(wall42)
+    walls.append(wall43)
+    walls.append(wall44)
+    walls.append(wall45)
+    walls.append(wall46)
+    walls.append(wall47)
+    walls.append(wall48)
+
+    return(walls)

gui/interface/simulations/fyp_simulation/ddqn_keras.py

@@ -0,0 +1,126 @@
+# File: ddqn_keras.py
+
+import numpy as np
+import tensorflow as tf
+from keras.models import Sequential, load_model
+from keras.layers import Dense
+from keras.optimizers import Adam
+
+class ReplayBuffer:
+    def __init__(self, max_size, input_shape, n_actions, discrete=False):
+        self.mem_size = max_size
+        self.mem_cntr = 0
+        self.discrete = discrete
+        self.state_memory = np.zeros((self.mem_size, input_shape))
+        self.new_state_memory = np.zeros((self.mem_size, input_shape))
+        dtype = np.int8 if self.discrete else np.float32
+        self.action_memory = np.zeros((self.mem_size, n_actions), dtype=dtype)
+        self.reward_memory = np.zeros(self.mem_size)
+        self.terminal_memory = np.zeros(self.mem_size, dtype=np.float32)
+
+    def store_transition(self, state, action, reward, state_, done):
+        index = self.mem_cntr % self.mem_size
+        self.state_memory[index] = state
+        self.new_state_memory[index] = state_
+        if self.discrete:
+            actions = np.zeros(self.action_memory.shape[1])
+            actions[action] = 1.0
+            self.action_memory[index] = actions
+        else:
+            self.action_memory[index] = action
+        self.reward_memory[index] = reward
+        self.terminal_memory[index] = 1 - done
+        self.mem_cntr += 1
+
+    def sample_buffer(self, batch_size):
+        max_mem = min(self.mem_cntr, self.mem_size)
+        batch = np.random.choice(max_mem, batch_size)
+
+        states = self.state_memory[batch]
+        actions = self.action_memory[batch]
+        rewards = self.reward_memory[batch]
+        states_ = self.new_state_memory[batch]
+        terminal = self.terminal_memory[batch]
+
+        return states, actions, rewards, states_, terminal
+
+class DDQNAgent:
+    def __init__(self, alpha, gamma, n_actions, epsilon, batch_size,
+                 input_dims, epsilon_dec=0.999995, epsilon_end=0.10,
+                 mem_size=25000, fname='ddqn_model.keras', replace_target=25):
+        self.action_space = [i for i in range(n_actions)]
+        self.n_actions = n_actions
+        self.gamma = gamma
+        self.epsilon = epsilon
+        self.epsilon_dec = epsilon_dec
+        self.epsilon_min = epsilon_end
+        self.batch_size = batch_size
+        self.model_file = fname
+        self.replace_target = replace_target
+        self.memory = ReplayBuffer(mem_size, input_dims, n_actions, discrete=True)
+       
+        self.brain_eval = Brain(input_dims, n_actions, batch_size)
+        self.brain_target = Brain(input_dims, n_actions, batch_size)
+
+    def remember(self, state, action, reward, new_state, done):
+        self.memory.store_transition(state, action, reward, new_state, done)
+
+    def choose_action(self, state):
+        state = np.array(state)[np.newaxis, :]
+        rand = np.random.random()
+        if rand < self.epsilon:
+            action = np.random.choice(self.action_space)
+        else:
+            actions = self.brain_eval.model.predict(state, verbose=0)
+            action = np.argmax(actions)
+        return action
+
+    def learn(self):
+        if self.memory.mem_cntr > self.batch_size:
+            state, action, reward, new_state, done = self.memory.sample_buffer(self.batch_size)
+            action_indices = np.dot(action, np.array(self.action_space, dtype=np.int8))
+            
+            q_next = self.brain_target.model.predict(new_state, verbose=0)
+            q_eval = self.brain_eval.model.predict(new_state, verbose=0)
+            q_pred = self.brain_eval.model.predict(state, verbose=0)
+
+            max_actions = np.argmax(q_eval, axis=1)
+            
+            q_target = np.copy(q_pred)
+            batch_index = np.arange(self.batch_size, dtype=np.int32)
+            q_target[batch_index, action_indices] = reward + self.gamma * q_next[batch_index, max_actions.astype(int)] * done
+
+            self.brain_eval.model.fit(state, q_target, verbose=0)
+            self.epsilon = max(self.epsilon * self.epsilon_dec, self.epsilon_min)
+
+    def update_network_parameters(self):
+        self.brain_target.copy_weights(self.brain_eval)
+
+    def save_model(self):
+        self.brain_eval.model.save(self.model_file)  # Save using .keras format
+        
+    def load_model(self):
+        self.brain_eval.model = tf.keras.models.load_model(self.model_file)
+        self.brain_target.model = tf.keras.models.load_model(self.model_file)
+        if self.epsilon == 0.0:
+            self.update_network_parameters()
+
+class Brain:
+    def __init__(self, NbrStates, NbrActions, batch_size=256):
+        self.NbrStates = NbrStates
+        self.NbrActions = NbrActions
+        self.batch_size = batch_size
+        self.model = self.createModel()
+    
+    def createModel(self):
+        model = Sequential()
+        model.add(Dense(256, activation='relu', input_shape=(self.NbrStates,)))
+        model.add(Dense(self.NbrActions, activation=tf.keras.activations.softmax))
+        model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))
+        return model
+    
+    def copy_weights(self, TrainNet):
+        variables1 = self.model.trainable_variables
+        variables2 = TrainNet.model.trainable_variables
+        for v1, v2 in zip(variables1, variables2):
+            v1.assign(v2.numpy())

gui/interface/simulations/neat_simulation.py

@@ -0,0 +1,33 @@
+import os
+import neat
+import pickle
+from .fyp1_simulation.newcar import run_simulation
+
+# Paths
+config_path = os.path.join(os.path.dirname(__file__), 'fyp1_simulation', 'config.txt')
+genome_path = os.path.join(os.path.dirname(__file__), 'fyp1_simulation', 'best_neat_genome.pkl')
+
+# Load NEAT config
+config = neat.config.Config(
+    neat.DefaultGenome,
+    neat.DefaultReproduction,
+    neat.DefaultSpeciesSet,
+    neat.DefaultStagnation,
+    config_path
+)
+
+# Load the best genome
+if not os.path.exists(genome_path):
+    raise FileNotFoundError(f"Best genome file not found at {genome_path}")
+
+with open(genome_path, "rb") as f:
+    best_genome = pickle.load(f)
+
+# print("\n✅ Best genome loaded successfully!")
+# print(f"Fitness: {best_genome.fitness}")
+
+# Wrap the genome in the expected format: list of tuples (genome_id, genome)
+test_genomes = [(0, best_genome)]
+
+# Run the simulation for testing
+run_simulation(test_genomes, config)

gui/interface/static/interface/css/all.min.css

@@ -0,0 +1,9 @@
+/*!
+ * Font Awesome Free 6.6.0 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ * Copyright 2024 Fonticons, Inc.
+ */
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